Screening Municipal Building Permissions for Sustainable Urban Renewal: An Interpretable Machine Learning Approach with Temporal Validation

Despite the importance of adaptive reuse for urban renewal, municipal records of changes in property destinations remain an untapped resource for understanding regulatory barriers. This study develops an interpretable machine learning (ML) framework to screen historical administrative non-approval using 42,350 municipal records from a mid-sized city in southern Chile spanning 2010 to 2025. Historical memory features were constructed exclusively from outcomes available before each application, thereby preventing temporal leakage. Records from 2010–2022 were used for training, 2023 was reserved for validation, and 2024–2025 constituted a locked test set. A logistic regression model combining auditable risk flags derived from GIRO (declared economic or functional activity) with historical property, address, and activity category information was selected by maximizing the F1-score (the harmonic mean of precision and recall) subject to a recall ≥ 0.85. At the validation-selected threshold of 0.285, the model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.778, a precision–recall area under the curve (PR-AUC) of 0.720, an F1-score of 0.719, recall of 0.887, and precision of 0.604. The model flagged 66.8% of applications for prioritized review. The proposed framework supports human-in-the-loop screening and institutional learning; it neither determines regulatory compliance nor automates municipal decisions.